Results 41 to 50 of about 136,207 (260)

Charting Endocrine Progenitors Across Species and Organs

Advanced Science, EarlyView.
Endocrine progenitors give rise to the hormone‐producing cells of the pancreas and intestine. Using single‐cell multiomics and proteomics, this study compares these progenitors across species, systems, and organs, mapping the conserved and species‐specific gene regulatory networks that guide their formation.
Changying Jing, Michael Sterr, Özüm Sehnaz Caliskan, Lama Saber, Nicole Katarina Rogers, Samuel Ogden, Kei Kozawa, Christos Karampelias, Jessica Jaki, Aimée Bastidas‐Ponce, Falk Janos Farkas, Oliver Czarnecki, Osvaldo Rodrigues Pereira, Perla Cota, Valerie Vandenbempt, Diego Balboa, Anika Böttcher, Natalie Krahmer, Mohammad Lotfollahi, Julie B. Sneddon, Heiko Lickert, Mostafa Bakhti   +21 more
wiley   +1 more source

Accelerating the Discovery of Proton Conducting Electrolytes via Machine Learning‐Enabled Literature Mining

Advanced Intelligent Discovery, EarlyView.
An end‐to‐end knowledge discovery framework is established to automate high‐precision property extraction from small, specialized literature corpora. Utilizing a domain‐specific bidirectional encoder representation from a transformer model and data augmentation, the system accurately extracts and structures electrolyte performance data, ultimately ...
Gaheun Shin, Seongho Lee, Seulchan Kim, Ho‐Il Ji, Joonhee Kang   +4 more
wiley   +1 more source

TF-IDF based decomposition of research problems in the domain of EBPR, ordered by cluster size.

, 2019
TF-IDF based decomposition of research problems in the domain of EBPR, ordered by cluster size.
Tim Rogers (6685226), Minh Nguyen Quang (6685223), Jan Hofman (295483), Ana B. Lanham (6685229)   +3 more
core   +1 more source

Results of SVM TF-IDF, TFPOS-IDF, W2V-TFPOSIDF for Yahya et al. (2012) dataset.

, 2020
Results of SVM TF-IDF, TFPOS-IDF, W2V-TFPOSIDF for Yahya et al. (2012) dataset.
Nazlia Omar (801714), Manal Mohammed (3453335)   +1 more
core   +1 more source

Context‐centric proactive information delivery for Knowledge Work support: Opportunities, challenges, and directions. An Annual Review of Information Science and Technology (ARIST) paper

Journal of the Association for Information Science and Technology, EarlyView.
Abstract Context‐centric proactive information delivery (PID) is a relatively underexplored domain within recommender systems (RS) aimed at enhancing Knowledge Workers' productivity by proactively providing relevant information during digital tasks.
Mahta Bakhshizadeh, Christian Jilek, Markus Schröder, Heiko Maus, Andreas Dengel   +4 more
wiley   +1 more source

Comprehensive Comparison of TF-IDF and Word2Vec in Product Sentiment Classification Using Machine Learning Models

Journal of Applied Informatics and Computing
Sentiment analysis supports data-driven decisions by turning product reviews into reliable polarity labels. We compare four text representations, TF-IDF, TF-IDF reduced via SVD, Word2Vec (trained from scratch), and a hybrid TF-IDF(SVD-300). Word2Vec, for
Asra Gretya Sinaga, Robet Robet, Octara Pribadi   +2 more
doaj   +1 more source

Aspect-Based Sentiment Analysis for Afaan Oromoo Movie Reviews Using Machine Learning Techniques

Applied Computational Intelligence and Soft Computing, 2023
Aspect-based sentiment analysis (ABSA) is the subfield of natural language processing that deals with essentially splitting data into aspects and finally extracting the sentiment polarity as positive, negative, or neutral.
Obsa Gelchu Horsa, Kula Kekeba Tune
doaj   +1 more source

Analisis Perbandingan Metode Pembobotan Kata Delta TF-IDF dan TF-IDF Terhadap Performansi Analisis Sentimen [PDF]

, 2019
Metode ekstraksi fitur yang umum digunakan untuk analisis sentimen yaitu pembobotan kata TF-IDF. Namun, metode TF-IDF memiliki kekurangan dalam menentukan pembobotan untuk analisis sentimen karena tidak dapat memberikan bobot yang berbeda terhadap kata ...
RIAN ADY RAIHANDIWAN
core  

Results of using KNN with TF-IDF, TFPOS-IDF, W2V-TFPOSIDF for the collected dataset.

, 2020
Results of using KNN with TF-IDF, TFPOS-IDF, W2V-TFPOSIDF for the collected dataset.
Nazlia Omar (801714), Manal Mohammed (3453335)   +1 more
core   +1 more source

Incremental refinement of relevance rankings: Balancing relevance depth and scope

Journal of the Association for Information Science and Technology, EarlyView.
Abstract Delivering both relevant and topically diverse results is a key challenge in information retrieval (IR). This study introduces a hybrid method that incrementally refines rankings by combining probabilistic topic modeling (latent dirichlet allocation [LDA]) with citation‐based pennant retrieval grounded in Relevance Theory (RT), optimizing for ...
Müge Akbulut, Yaşar Tonta
wiley   +1 more source